Nielsen BASES has been the industry-standard concept testing and volumetric forecasting system for CPG product launches since the 1980s. For most large consumer packaged goods companies, a BASES score is a required gate in the innovation process: concepts that score below the category norm do not advance, and concepts that score above it receive development investment. The system works. BASES-validated launches outperform unvalidated launches. But the system also has well-documented limitations that are driving a growing number of innovation teams to evaluate alternatives.
This guide compares the major BASES alternatives available in 2026 across six dimensions that matter most to CPG innovation leaders: diagnostic depth, speed, cost, forecast precision, methodology flexibility, and integration with modern innovation workflows. The goal is not to declare a winner but to help you match the right platform to your specific bottleneck.
Why Teams Leave BASES
Before evaluating alternatives, it is worth understanding the specific pain points that drive the search. In conversations with hundreds of CPG innovation professionals, five complaints recur consistently:
Cost per study. A full BASES study with volumetric forecasting typically costs $50,000 to $150,000 depending on the category, number of concepts tested, and geographic scope. For organizations testing multiple concepts per quarter, the annual spend on BASES alone can exceed $500,000. This cost structure limits the number of concepts that can be tested, which means many promising ideas never receive consumer validation because the testing budget is exhausted on the concepts furthest along in development.
Timeline. A standard BASES study takes 8-12 weeks from briefing to final report. In an innovation environment where stage-gate processes demand consumer validation at every phase, this timeline creates a bottleneck that slows the entire pipeline. Teams report that they make go/no-go decisions without BASES data simply because they cannot wait 8-12 weeks for each gate.
Diagnostic depth. BASES produces a volumetric forecast and a set of normative scores: purchase intent, value perception, uniqueness, relevance. What it does not produce is the diagnostic reasoning behind those scores. A concept that scores below norm on purchase intent receives a number but not an explanation. The innovation team knows the concept is underperforming but does not know why, which means they cannot fix it. They can only kill it or advance it with risk.
Methodology rigidity. The BASES methodology is standardized, which ensures comparability across studies and categories. But standardization also means the methodology cannot adapt to novel concepts, unusual categories, or specific research questions. Teams with non-standard concepts report that the BASES framework does not capture the dimensions that matter most for their specific innovation.
Data accessibility. BASES results are delivered as a static report. The underlying data is not easily searchable, reusable, or cross-referenceable across studies. When a team launches a new concept in the same category, they start from scratch rather than building on the cumulative learning from previous BASES studies.
The Platform Landscape: Two Categories of Alternatives
BASES alternatives cluster into two fundamentally different categories based on what they replace:
Category 1: Quantitative Replacements. These platforms replicate the core BASES function, survey-based concept scoring with normative benchmarks and, in some cases, volumetric forecasting, but do so faster and cheaper through technology-driven efficiency. Zappi, Suzy Speaks, and Ipsos InnoQuest are the leading platforms in this category.
Category 2: Qualitative-Depth Alternatives. These platforms do not replicate BASES scoring. Instead, they replace the quantitative surface metrics with qualitative depth that explains the “why” behind consumer reactions. AI-moderated concept testing platforms like User Intuition fall into this category. They are not BASES replacements in the volumetric sense; they are BASES complements that solve the diagnostic depth gap.
The right alternative depends on your specific bottleneck:
| Your Bottleneck | The Right Alternative Category |
|---|---|
| BASES is too expensive for the volume of testing we need | Quantitative Replacement |
| BASES is too slow for our innovation timeline | Quantitative Replacement or Qualitative-Depth |
| BASES tells us the score but not how to improve the concept | Qualitative-Depth |
| We need volumetric forecasts but cannot afford BASES pricing | Quantitative Replacement |
| We are killing concepts that might succeed with optimization | Qualitative-Depth |
| We need consumer evidence for retailer sell-in, not just a forecast | Qualitative-Depth |
Many CPG companies are adopting a dual-platform approach: using a qualitative-depth platform for early-stage concept optimization and iterative testing, then using BASES or a quantitative alternative for the final volumetric forecast before commercialization. This approach combines diagnostic depth with forecast precision at a lower total cost than running multiple BASES studies.
Zappi: Automated Survey-Based Concept Testing
Zappi is the most direct BASES alternative in the quantitative replacement category. It offers automated survey-based concept testing with normative benchmarks, rapid turnaround, and significantly lower cost than BASES.
Methodology. Zappi uses a structured survey methodology with concept exposure followed by rating scales for purchase intent, uniqueness, relevance, and other standard concept metrics. The platform includes normative databases that allow benchmark comparison against category norms, similar to BASES.
Speed. Zappi delivers results in days rather than weeks, a significant improvement over BASES timelines. The automation of survey programming, fielding, and reporting removes the manual steps that create the BASES bottleneck.
Cost. Zappi studies typically cost $5,000-$15,000, compared to $50,000-$150,000 for BASES. This price point allows teams to test more concepts more frequently, which improves the overall hit rate of the innovation pipeline.
Limitations. Zappi replicates the fundamental limitation of all survey-based concept testing: it produces scores without diagnostic depth. A concept that scores below norm on Zappi receives the same numeric diagnosis it would from BASES, faster and cheaper, but still without the qualitative explanation of why consumers reacted that way. Teams still face the “now what?” problem when a concept underperforms.
Best for: Organizations that need normative benchmark comparison for portfolio decisions and stage-gate governance but cannot justify BASES cost and timeline for every concept.
Suzy Speaks: AI-Powered Survey With Qualitative Add-On
Suzy (formerly Suzy Speaks) positions itself as a consumer insights platform that combines survey-based quantitative testing with shorter qualitative interactions. The platform targets marketing and insights teams who need fast answers to specific questions.
Methodology. Suzy’s core methodology is survey-based with the option to add short video or text-based qualitative responses. The qualitative component is typically limited to 5-10 minute interactions focused on specific questions rather than full-depth interviews.
Speed. Suzy delivers results within 24-48 hours for standard survey-based studies. The speed is comparable to Zappi and represents a significant improvement over BASES timelines.
Cost. Suzy operates on a subscription model with annual commitments, typically ranging from $50,000-$200,000 per year depending on usage volume. Individual study costs are lower than BASES but the annual commitment is higher than per-study platforms.
Limitations. Suzy’s qualitative component is limited in depth compared to dedicated AI-moderated interview platforms. The 5-10 minute interaction window does not allow for the 5-7 levels of laddering that produce true diagnostic depth. Consumer responses tend to be surface-level and lack the behavioral specificity that comes from 30+ minute depth interviews. Additionally, Suzy’s interview methodology is marketing-led rather than research-methodology-led, which can introduce bias in the questioning approach.
Best for: Marketing teams that need fast quantitative concept screening with light qualitative color and are willing to commit to an annual platform subscription.
Ipsos InnoQuest: Enterprise Concept Testing With Volumetric Forecasting
Ipsos InnoQuest is the closest methodological competitor to BASES, offering concept testing with volumetric forecasting capabilities backed by Ipsos’s global normative databases.
Methodology. InnoQuest uses a proprietary survey methodology with volumetric modeling that accounts for awareness, distribution, trial, and repeat purchase. The methodology is conceptually similar to BASES but uses different calibration models and normative benchmarks.
Speed. InnoQuest timelines are typically 6-10 weeks, marginally faster than BASES but still measured in weeks rather than days. The speed improvement comes from Ipsos’s investment in automation, but the fundamental survey-plus-modeling workflow limits how much the timeline can compress.
Cost. InnoQuest pricing is typically 20-40% lower than BASES for comparable studies, ranging from $30,000 to $100,000 depending on scope. The cost advantage comes from Ipsos’s scale and the partial automation of their workflow.
Limitations. InnoQuest shares the diagnostic depth limitation of all survey-based systems. The volumetric forecast is arguably as robust as BASES for major CPG categories, but the concept evaluation component still produces scores rather than explanations. Innovation teams get a forecast but not the consumer reasoning needed to optimize concepts that fall short.
Best for: Enterprise CPG companies that require volumetric forecasting with normative benchmarks and prefer an alternative to Nielsen’s ecosystem for competitive or procurement reasons.
User Intuition: AI-Moderated Depth Interviews for Diagnostic Concept Testing
User Intuition approaches concept testing from a fundamentally different angle than BASES and its quantitative alternatives. Instead of producing a normative score or a volumetric forecast, it produces diagnostic depth: the specific reasons why consumers react the way they do, expressed in their own words, with 5-7 levels of probing that uncover the motivations, barriers, and competitive comparisons that surveys cannot access.
Methodology. AI-moderated 1:1 interviews lasting 30+ minutes with each participant. The AI moderator applies consistent laddering methodology across every conversation, probing beneath surface reactions to uncover the behavioral drivers behind concept evaluation. Studies typically include 100-300 participants recruited from a panel of 4M+ verified consumers or from the brand’s own customer base.
Speed. Results in 48-72 hours. A concept test that would take 8-12 weeks through BASES or 6-10 weeks through InnoQuest completes in three days. This speed allows iterative testing: test a concept, optimize based on findings, and re-test the revised concept within a single work week.
Cost. Starting at $200 for 20 interviews ($10/interview), with full studies of 100-300 consumers costing $1,000-$6,000. This represents a 93-96% cost reduction compared to BASES and an 80-90% reduction compared to Zappi.
Diagnostic Depth. This is the core differentiator. When a concept underperforms, the interview data explains exactly why: which aspect of the concept triggered skepticism, what competitive alternative the consumer prefers and why, what price threshold they would need to see, what claim would increase their confidence, and how the concept fits (or does not fit) into their actual purchase behavior. This diagnostic depth transforms concept testing from a scoring exercise into a concept optimization tool.
Intelligence Hub. Every interview is stored as searchable, cross-referenceable data in the Customer Intelligence Hub. This means that findings compound across studies: the fifth concept test in a category can reference and build on the first four, creating institutional memory that static reports cannot provide.
Limitations. User Intuition does not produce a volumetric forecast in the BASES format. Organizations that require a specific volume number for financial modeling and retailer negotiations will still need a quantitative forecasting layer. The platform complements rather than replaces volumetric forecasting for organizations where that forecast is a required gate.
Best for: Innovation teams whose bottleneck is understanding the “why” behind consumer reactions, CPG companies that need to optimize concepts before committing to expensive quantitative validation, and organizations building a cumulative consumer intelligence asset across studies and categories.
Head-to-Head Comparison Matrix
| Dimension | BASES | Zappi | Suzy | Ipsos InnoQuest | User Intuition |
|---|---|---|---|---|---|
| Primary output | Volumetric forecast + scores | Normative scores | Scores + light qual | Volumetric forecast + scores | Diagnostic depth + consumer language |
| Diagnostic depth | Low | Low | Low-Medium | Low | High |
| Volumetric forecasting | Yes (industry standard) | No (benchmark comparison) | No | Yes | No (qualitative demand signals) |
| Speed | 8-12 weeks | 2-5 days | 1-2 days | 6-10 weeks | 48-72 hours |
| Cost per study | $50K-$150K | $5K-$15K | Subscription-based | $30K-$100K | $200-$6,000 |
| Interview depth | 10-15 min survey | 10-15 min survey | 5-10 min | 10-15 min survey | 30+ min AI-moderated |
| Sample per study | 200-600 | 200-400 | 200-400 | 200-600 | 100-300 |
| Normative benchmarks | Extensive | Growing | Limited | Extensive | No (verbatim-based analysis) |
| Knowledge accumulation | Static reports | Dashboard | Dashboard | Static reports | Searchable Intelligence Hub |
| Panel access | Nielsen panels | Integrated panel | Integrated panel | Ipsos panels | 4M+ panel + first-party |
| Languages | 30+ | 20+ | English primary | 40+ | 50+ |
| Iterative testing | Impractical (cost/time) | Feasible | Feasible | Impractical (cost/time) | Designed for iteration |
Choosing Your Platform: Decision Framework
The right platform choice depends on three questions:
Question 1: Do you need a volumetric forecast, or do you need diagnostic understanding?
If your organization requires a specific volume number to justify commercialization investment, you need BASES, InnoQuest, or a similar quantitative forecasting system. Qualitative platforms do not produce this output.
If your challenge is that concepts are failing or underperforming because you do not understand consumer reactions well enough to optimize them, you need diagnostic depth that only AI-moderated interviews provide.
Most sophisticated CPG organizations need both. The efficient approach is to use diagnostic testing early and often (during ideation, screening, and optimization) and quantitative forecasting once (at the final pre-commercialization gate).
Question 2: What is your testing volume and budget?
If you test 2-3 concepts per year and have a $300K+ research budget, BASES or InnoQuest provides proven forecasting. If you test 10-20+ concepts per year and need to validate more ideas with less budget, platforms like Zappi or User Intuition enable higher testing volume at a fraction of the cost.
Question 3: How does concept testing connect to the rest of your innovation process?
If concept testing is a standalone gate, any platform can serve. If concept testing needs to feed claims development, retailer sell-in, packaging design, and R&D briefs, the diagnostic depth and verbatim language from AI-moderated interviews provides more actionable inputs across more business functions.
The Dual-Platform Approach: Qualitative Depth + Quantitative Forecasting
The emerging best practice among forward-thinking CPG companies is a dual-platform approach that combines diagnostic depth testing with quantitative forecasting. The model works as follows:
Stage 1-3 (Ideation through Optimization): AI-Moderated Concept Testing. Use User Intuition or a similar qualitative-depth platform for all early-stage concept testing. Test 10-20 concepts in screening, optimize the top 3-5 through iterative testing, and arrive at 1-2 fully optimized concepts. Total cost: $2,000-$10,000. Total time: 2-4 weeks.
Stage 4 (Pre-Commercialization): Quantitative Forecasting. Submit the optimized concept(s) to BASES, InnoQuest, or Zappi for volumetric forecasting. Because the concept has already been optimized through diagnostic testing, the probability of passing the quantitative gate is dramatically higher. Total cost: $5,000-$150,000 depending on platform. Total time: 1-12 weeks depending on platform.
Total program cost: $7,000-$160,000, compared to $100,000-$450,000 for running multiple BASES studies across the same number of concepts.
Total program time: 3-16 weeks, compared to 24-48 weeks for sequential BASES studies.
Outcome improvement: Organizations using the dual-platform approach report 40-60% higher pass rates at the quantitative forecasting gate because concepts arrive already optimized based on consumer diagnostic feedback. This reduces the number of concepts killed at the expensive final gate, which means less development investment wasted on concepts that fail quantitative validation.
The dual-platform approach is not a compromise. It is a superior methodology that combines the strengths of qualitative depth (understanding, optimization, consumer language) with the strengths of quantitative forecasting (normative comparison, volumetric prediction) while eliminating the weaknesses of using either approach alone.